Key observations: We compare the test errors for the pruned networks with respective reweighting schemes for both datasets as in Figure REF . We observe that (1) DPP edge pruned network significantly outperforms all other pruning methods, including DIVNET (consistent with Conjecture REF ). (2) Interestingly, we find that importance node pruning with reweighting performs better than DIVNET, which was not explored in [1]}. (3) Furthermore, unlike other pruning methods, our sparse DPP edge pruned network generalizes better than the unpruned dense network for both the datasets (consitent with the teacher student framework, see Figure REF C). In literature, such sparse sub-networks of a dense network with better generalization ability is achieved via iterative pruning method [2]}, which is time consuming. However, our approach gives rise to such sparse network without reinitializing and retraining but only reweighting (therefore, loosely connecting to Lottery Ticket Hypothesis [3]} but not exactly) adding to the novelty of our approach.
